In recent years, computer vision and deep learning technologies have made significant advancements, opening up new possibilities for the development of automated food recognition systems. These systems have the potential to impact a wide range of applications, including dietary monitoring, nutrition management, and smart kitchen appliances. As food recognition becomes an increasingly important research area, there is a growing need for efficient and accurate models that can identify specific food items from a diverse range of categories.
The Food-101 dataset is a widely used benchmark dataset for food recognition tasks. It contains 101 different food categories, each with 1000 images. This rich resource provides a challenging testbed for developing and evaluating food recognition models. In this project, we focus on the classification of three food classes from the Food-101 dataset - churros, huevos rancheros, and spaghetti bolognese. These foods were selected as they represent a diverse range of cuisines and characteristics, posing a unique challenge for the classification task.
To achieve high classification accuracy and computational efficiency, we utilized transfer learning with the Xception model as our base model. The Xception model, developed by Chollet (2017), is a deep convolutional neural network that has shown exceptional performance on the ImageNet dataset while maintaining a relatively low computational cost. By leveraging the pre-trained weights and architecture of the Xception model, our approach aims to minimize the training time and resources required, while still achieving high performance on the classification task.
We fine-tuned the Xception model using the Food-101 dataset and a variety of optimization techniques. Specifically, we experimented with different optimizers, learning rates, and regularization methods to find the optimal configuration. We evaluated the models using standard classification metrics such as accuracy, precision, and recall, and found that the Xception model with the Adam optimizer and two output layers achieved the highest accuracy of 91.2% on the test set.
Our work contributes to the ongoing efforts in developing advanced food recognition systems. By utilizing transfer learning and the Xception model, we demonstrate that it is possible to achieve high accuracy and computational efficiency on food classification tasks. Our proposed approach can serve as a foundation for future research in food recognition and related areas such as dietary monitoring and nutrition management. Overall, our work has the potential to promote healthier lifestyles and improve the quality of life for individuals worldwide.
